Writing
6posts found
AI removed code generation as the constraint, and the bottleneck moved downstream to review. What the data says, how teams are responding, and why the person who can tell when the output is wrong is now both the bottleneck and the multiplier.
You don't need to be a researcher to make smart choices when downloading models. Here's what quantization actually is, why it barely hurts quality, and how to pick the right version on Ollama or Hugging Face.
Prompt-tuning can't fix systematic agent failures. Harness engineering, the scaffolding around the model, is where the real leverage is. Same model, 6x performance, and why the engineer's job is shifting.
The problem at the heart of every AI agent, why the obvious solutions fall apart, and an honest look at what the current wave of memory infrastructure gets right and wrong.
When your product serves multiple personas, the fastest way to slow down is to pretend they're all the same system. How we split by domain, change-rate, and risk at Enqurious.
The story of how an MVP grew into a platform with 7k daily active users while the engineering team stayed tiny. What we built, and what we deliberately refused to build.